Methodology for vehicle safety development and assessment accounting for occupant response variability to human and non-human factors

Abstract

The use of standardized anthropomorphic test devices and test conditions prevent current vehicle development and safety assessments from capturing the breadth of variability inherent in real-world occupant responses. This study introduces a methodology that overcomes these limitations by enabling the assessment of occupant response while accounting for sources of human- and non-human-related variability. Although the methodology is generic in nature, this study explores the methodology in its application to human response in far-side motor vehicle crashes as an example. A total of 405 human body model simulations were conducted in a mid-sized sedan vehicle environment to iteratively train two neural networks to predict occupant head excursion and thoracic injury as a function of occupant anthropometry, impact direction and restraint configuration. The neural networks were utilized in Monte Carlo simulations to calculate the probability of head-to-intruding-door impacts and thoracic AIS 3+ as a function of the restraint configuration. This analysis indicated that the vehicle used in this study would lead to a range of 667 to 2,448 head-to-intruding-door impacts and a range of 3,041 to 3,857 cases of thoracic AIS 3+ in the real world, depending on the seatbelt load limiter. These real-world results were later successfully validated using United States field data. This far-side assessment illustrates how the methodology incorporates the human and non-human variability, generates response surfaces that characterize the effects of the variability, and ultimately permits vehicle design considerations and injury predictions appropriate for real-world field conditions.

Keywords:

• Response surface
• Monte Carlo analysis
• vehicle safety
• far-side impacts
• human body models

Acknowledgments

The authors would like to thank Autoliv Research and Honda R&D Americas, Inc. for the financial support provided to complete this study. It should be noted that the sponsors did not have an active involvement in the study and that the views expressed in this article are those of the authors and do not necessarily represent or reflect the views of the sponsors.

Disclosure statement

The authors declare no conflict of interest.

Figure A1. Scatter plot of the actual vs predicted head excursion and thoracic AIS 3+.

Figure A2. Evolution of the NN training and testing error for the prediction of maximum lateral head excursion.

Figure A3. Evolution of the NN training and testing error for the prediction of thoracic AIS 3+.

Figure A4. Boxplots with the AIS 3+ testing error distributions obtained with the various additional regression models created to assess model convergence.